class: title-slide <br><br><br> # Calculating Vector Accelerations Using American Football GPS Data ## ECON 5253 Final Project ### Steven Plaisance ### University of Oklahoma --- # Introduction - American Football GPS Data - Provides raw positional data for each athlete - Sampled at 10 Hz (10 obervations per second) - Currently used primarily to monitor workload and performance indicators - The NFL is working to bridge the gap between GPS data and football analytics (Big Data Bowl) - Possible applications include In-game decision-making, roster construction and draft analytics - Vector Accelerations - The data includes scalar acceleration, so magnitude without respect to the direction of movement. - Goal: calculate acceleration vectors with respect to the athlete's orientation and movement. - Find propulsion (positive acceleration) and braking (negative acceleration) in each of the four directions - Forward, Backward, Left and Right - This approach provides more individualized insights --- # Data - The raw data includes the following variables sampled at 10Hz - athlete_id: A uid describing the athlete being recorded - stream_type: Specifies whether the data was recorded via satellite positioning or local positioning - x: field x coordinates (meters) - y: field y coordinates (meters) - ts: POSIX time in seconds since the start of the epoch - cs: Observation time offset in centiseconds - face: the magnetic facing of the unit (degrees) - v: velocity (meters per second) - a: acceleration (meters per second per second) - pq: positional quality (percentage) --- # Data (cont.) .center[ <img src="data:image/png;base64,#intoPic.png" width="75%" /> Source: Hassan Inayali, Aaron White, and Daniel Hocevar, NFL Big Data Bowl 2023 ] --- # Methods - Our vectors will be based on the difference between the direction the athlete is facing (face) and the direction they are moving. - First we must calculate a new variable alpha, which represents the direction the athlete is moving. - Done via the two-argument arctangent function, using instaneous change in x and y coordinates as the arguments - Next we calculate a new variable theta, the difference between face and alpha. - Now use theta to calculate a series of helper variables signaling what proportion of the current movement is in each of the four planes - Theta = 0 degrees would be 100% forward - Theta = 45 degrees would be 50% forward, 50% right, etc. - Simply multiply the helper variables by scalar acceleration to find local vectors. - These local vectors can now be used in place of the scalar accelerations in pre-existing GPS summaries --- # Results (Running Back) .center[ <img src="data:image/png;base64,#Results2.png" width="85%" /> ] --- # Results (Cornerback) .center[ <img src="data:image/png;base64,#results.png" width="85%" /> ] --- # Discussion By creating more specific acceleration vectors, we expand the analysis possibilities, both in terms of physical performance and in-game production. Some possible next steps: - Physical Performance - Monitor specific acceleration patterns of a rehab athlete based on their injury - Monitor performance changes in different directions based on position groups - Research relationship between chronic overuse in one direction and subsequent injury - Research relationship between different training programs and long-term performance changes - In-Game Production - Identify athletes' strengths and weaknesses in various movement types to better inform scheme and personnel decision-making - Which running backs and receivers cut faster toward their left/right? - Which defensive backs excel at backpedaling vs. forward propulion? (Corner vs. Safety) - Using NFL data, research relationship between various movement types and long-term NFL success.